Help extracting rows based on unique column 1 data

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I have a large MxN matrix of data from which i would like to extract specific rows of data.
The first column of the matrix contains a chronological timebase in seconds, in ascending order.
t1 a1 b1 .. n1
t2 a2 b2 .. n2
:
talpha ..
:
tbeta ..
:
tgamma ..
:
tm am bm .. nm
I know the unique times of 3 events (talpha, tbeta, tgamma) that occur in column 1.
How do i extract the rows in chronological order based on the knowledge of their unique times?
Tried categorical with valueset and ordinal but no luck!
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Brantosaurus
Brantosaurus on 19 Nov 2022
Moved: Voss on 19 Nov 2022
Not THAT large!!!
Maybe 10,000 x 20 at most

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Accepted Answer

Star Strider
Star Strider on 19 Nov 2022
There is a lot I don’t unerstand about this problem.
However, if you want to find the unique occurrences of each of the ‘talpha’ , ‘tbeta’ and ‘tgamma’ so that they return the indices of the first incidence of each value, use the unique function with the first two outputs (the unique values, and the indices of the first instance of each value) with the 'stable' argument (to prvent sorting the result).
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More Answers (1)

Campion Loong
Campion Loong on 30 Nov 2022
I would first put my data in a timetable. It is specifically built for time based data like yours.
% Make some pretend data for your a#/b#/c# numbers
A = rand(10000,1); B = rand(10000,1); C = rand(10000,1);
% Use timetable. Here I assume regular timesteps. If not, use the
% 'RowTimes' N/V pair instead
tt = timetable(A, B, C, 'TimeStep', seconds(1))
tt = 10000×3 timetable
Time A B C ________ ________ _______ ________ 00:00:00 0.046518 0.25765 0.11216 00:00:01 0.021558 0.19586 0.078626 00:00:02 0.325 0.81709 0.9364 00:00:03 0.11313 0.10249 0.36468 00:00:04 0.65928 0.43818 0.4145 00:00:05 0.59172 0.30821 0.52174 00:00:06 0.39627 0.23423 0.42336 00:00:07 0.42042 0.62615 0.8902 00:00:08 0.62093 0.60716 0.098711 00:00:09 0.030026 0.18906 0.49313 00:00:10 0.5556 0.51185 0.6461 00:00:11 0.65488 0.38215 0.59968 00:00:12 0.48795 0.79178 0.69614 00:00:13 0.040898 0.99794 0.59517 00:00:14 0.17782 0.81708 0.12582 00:00:15 0.64031 0.78373 0.81385
If you know the exact time of your unique events
t_alpha = seconds(10);
t_beta = minutes(150);
t_gamma = hours(2);
% Index directly with time. Get all the rows in one go like below, or
% separately as your preferred:
tt([t_alpha; t_beta; t_gamma], :)
ans = 3×3 timetable
Time A B C ________ _______ _______ _______ 00:00:10 0.5556 0.51185 0.6461 02:30:00 0.44544 0.32722 0.23712 02:00:00 0.48217 0.21062 0.37704
Now, if you are not sure the 'exact' time (e.g. your data has some noise)
tt.Time = tt.Time + milliseconds(randn(10000,1)); % mix in some made-up noise
% Exact time index will turn up nothing ...
tt([t_alpha; t_beta; t_gamma], :)
ans = 0×3 empty timetable
% Use withtol to index with time tolerance:
wt = withtol([t_alpha; t_beta; t_gamma],seconds(0.5))
wt =
timetable withtol subscript: Select timetable rows matching the following times: 10 sec 9000 sec 7200 sec with tolerance of +/- 0.5 sec See Select Times in Timetable.
tt(wt, :)
ans = 3×3 timetable
Time A B C ________ _______ _______ _______ 00:00:10 0.5556 0.51185 0.6461 02:30:00 0.44544 0.32722 0.23712 02:00:00 0.48217 0.21062 0.37704

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